code: def normalize_laf(LAF: Tensor, images: Tensor) -> Tensor:
      """Normalize LAFs to [0,1] scale from pixel scale. See below:
  
          B,N,H,W = images.size()
          MIN_SIZE =  min(H - 1, W -1)
          [a11 a21 x]
          [a21 a22 y]
          becomes:
          [a11/MIN_SIZE a21/MIN_SIZE x/(W-1)]
          [a21/MIN_SIZE a22/MIN_SIZE y/(H-1)]
  
      Args:
          LAF: :math:`(B, N, 2, 3)`
          images: :math:`(B, CH, H, W)`
  
      Returns:
          the denormalized LAF: :math:`(B, N, 2, 3)`, scale in image percentage (0, 1)
      """
      KORNIA_CHECK_LAF(LAF)
      _, _, h, w = images.size()
      wf = float(w - 1)
      hf = float(h - 1)
      min_size = min(hf, wf)
      coef = (
          torch.ones(1, 1, 2, 3, dtype=LAF.dtype, device=LAF.device)
          / min_size
      )
      coef[0, 0, 0, 2] = 1.0 / wf
      coef[0, 0, 1, 2] = 1.0 / hf
      return coef.expand_as(LAF) * LAF
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  from kornia.core import Tensor, concatenate, cos, sin, stack, tensor, zeros
  from kornia.core.check import KORNIA_CHECK_LAF, KORNIA_CHECK_SHAPE
  from kornia.geometry.conversions import angle_to_rotation_matrix, convert_points_from_homogeneous, rad2deg
  from kornia.geometry.linalg import transform_points
  from kornia.geometry.transform import pyrdown
label_desc: Use image node_id 13 for calling a global function with example usage: normalize_laf(LAF, images) and returns: unknown